应用ICEEMDAN和SVM的行星齿轮箱故障诊断

王浩楠; 崔宝珍; 彭智慧; 任川

doi:10.13433/j.cnki.1003-8728.20220020

应用ICEEMDAN和SVM的行星齿轮箱故障诊断

doi: 10.13433/j.cnki.1003-8728.20220020

中北大学机械工程学院, 太原 030051

基金项目:

国家自然科学基金项目 51175480

山西省重点研发计划(国际合作)项目 201903D421008

中北大学先进制造技术山西省重点实验室开放课题研究基金 XJZZ202007

详细信息

作者简介:
王浩楠(1997-), 硕士研究生, 研究方向为现场测试及机械故障诊断, 1872182205@qq.com

通讯作者:
崔宝珍, 副教授, 硕士生导师, 744212889@qq.com

中图分类号: TH132.41
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- 被引次数: 0
出版历程
- 收稿日期: 2021-04-27
- 刊出日期: 2023-01-25

Fault Diagnosis of Planetary Gearbox using ICEEMDAN and SVM

College of Mechanical Engineering, North University of China, Taiyuan 030051, China

摘要

摘要: 针对行星齿轮箱复合故障准确分类问题，应用了改进自适应噪声完备集合经验模态分解(ICEEMDAN)和支持向量机(SVM)相结合的故障诊断方法。首先，将行星齿轮箱的不同故障信号分别进行ICEEMDAN分解，得到各阶内禀模态函数(IMF); 其次，利用各阶IMF分量与原信号的相关性大小，剔除虚假的IMF分量; 最后，以优选IMF分量的多尺度模糊熵均值作为特征向量，输入到多分类SVM中进行故障分类，分类准确率高达100%，实验结果证明了该方法的可行性。
- 改进自适应噪声完备集合经验模态分解 /
- 频域互相关 /
- 多尺度模糊熵 /
- 支持向量机 /
- 行星齿轮箱故障
Abstract: Aiming at the problem of accurate classification of compound faults of planetary gearboxes, a fault diagnosis method combining improved adaptive noise complete set empirical mode decomposition (ICEEMDAN) and support vector machine (SVM) is proposed in this study. First, the different fault signals of the planetary gearbox are decomposed by ICEEMDAN to obtain the intrinsic mode function (IMF) of each order. Second, the correlation between the IMF component of each order and the original signal is used to remove the false IMF component. Finally, the multi-scale fuzzy entropy average value of the preferred IMF component is used as the feature vector and input into the multi-class SVM to accomplish fault classification. The classification accuracy is as high as 100%. The experimental results prove the feasibility of this method.
- ICEEMDAN /
- frequency domain cross-correlation /
- multi-scale fuzzy entropy /
- SVM /
- planetary gearbox failure

HTML全文

图 1 CEEMDAN与ICEEMDAN分解对比

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图 2 HFXZ-I行星齿轮箱故障诊断试验平台

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图 3 齿轮故障工况图

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图 4 传感器测点布置位置

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图 5 行星齿轮箱正常工况ICEEMDAN分解结果

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图 6 正常工况下各IMF分量的频域互相关系数

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图 7 EMD分解SVM分类预测图

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图 8 CEEMDAN分解SVM分类预测图

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图 9 ICEEMDAN分解SVM分类预测图

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表 1 行星齿轮箱4种工况的部分特征向量

工况	IMF₁	IMF₂	IMF₃	IMF₄	IMF₅	IMF₇
1	0.169 0.168 0.169 0.160 0.165	0.233 0.235 0.224 0.232 0.225	0.356 0.288 0.373 0.311 0.354	0.566 0.575 0.550 0.571 0.527	0.421 0.447 0.416 0.453 0.463	0.362 0.374 0.343 0.354 0.366
2	0.094 0.091 0.085 0.096 0.081	0.143 0.145 0.140 0.148 0.145	0.343 0.310 0.333 0.287 0.309	0.426 0.421 0.386 0.447 0.401	0.453 0.542 0.554 0.510 0.477	0.374 0.350 0.379 0.306 0.357
3	0.056 0.054 0.059 0.056 0.059	0.139 0.125 0.132 0.129 0.121	0.348 0.287 0.311 0.312 0.318	0.469 0.474 0.469 0.479 0.456	0.462 0.453 0.554 0.469 0.521	0.345 0.371 0.352 0.382 0.392
4	0.048 0.050 0.050 0.055 0.046	0.163 0.151 0.146 0.157 0.156	0.288 0.282 0.280 0.304 0.304	0.468 0.434 0.432 0.441 0.408	0.401 0.362 0.404 0.320 0.413	0.356 0.290 0.370 0.384 0.304

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表 2 3种方法分类对比

特征提取方法	分类准确率/%	每种工况用时/s
EMD CEEMDAN ICEEMDAN	90 95 100	154.465 726.077 554.869

下载: 导出CSV

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